The Algorithmic Imprint
Upol Ehsan, Ranjit Singh, Jacob Metcalf, Mark O. Riedl

TL;DR
This paper introduces the concept of 'algorithmic imprint' to describe how the effects of algorithms persist beyond their removal, using a case study of algorithmic grading in Bangladesh to analyze infrastructural, social, and individual impacts.
Contribution
It operationalizes the notion of algorithmic imprint and demonstrates its significance through a detailed case study, highlighting implications for design and governance.
Findings
Algorithmic impacts persist after removal, creating lasting imprints.
Global North algorithms disproportionately affect Global South stakeholders.
Imprint-awareness can improve impact assessment and AI governance.
Abstract
When algorithmic harms emerge, a reasonable response is to stop using the algorithm to resolve concerns related to fairness, accountability, transparency, and ethics (FATE). However, just because an algorithm is removed does not imply its FATE-related issues cease to exist. In this paper, we introduce the notion of the "algorithmic imprint" to illustrate how merely removing an algorithm does not necessarily undo or mitigate its consequences. We operationalize this concept and its implications through the 2020 events surrounding the algorithmic grading of the General Certificate of Education (GCE) Advanced (A) Level exams, an internationally recognized UK-based high school diploma exam administered in over 160 countries. While the algorithmic standardization was ultimately removed due to global protests, we show how the removal failed to undo the algorithmic imprint on the sociotechnical…
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